Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations28539
Missing cells56259
Missing cells (%)9.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 MiB
Average record size in memory194.1 B

Variable types

Text3
Numeric10
Categorical6
Boolean1

Alerts

ReservedParking is highly overall correlated with possessionStatusHigh correlation
ageofcons is highly overall correlated with possessionStatus and 1 other fieldsHigh correlation
bathrooms is highly overall correlated with bedrooms and 5 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
carpetArea is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
carpetAreaSqft is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
coveredArea is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
coveredAreaSqft is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
floorNumber is highly overall correlated with possessionStatus and 1 other fieldsHigh correlation
noOfLifts is highly overall correlated with totalFloorNumberHigh correlation
ownershipType is highly overall correlated with possessionStatusHigh correlation
possessionStatus is highly overall correlated with ReservedParking and 4 other fieldsHigh correlation
price is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
totalFloorNumber is highly overall correlated with floorNumber and 3 other fieldsHigh correlation
transactionType is highly overall correlated with ageofcons and 1 other fieldsHigh correlation
possessionStatus is highly imbalanced (99.1%) Imbalance
ownershipType is highly imbalanced (52.7%) Imbalance
propertyType is highly imbalanced (85.8%) Imbalance
carpetArea has 5695 (20.0%) missing values Missing
coveredArea has 723 (2.5%) missing values Missing
carpetAreaSqft has 5697 (20.0%) missing values Missing
possessionStatus has 5711 (20.0%) missing values Missing
ownershipType has 12342 (43.2%) missing values Missing
ageofcons has 3999 (14.0%) missing values Missing
noOfLifts has 15210 (53.3%) missing values Missing
ReservedParking has 5824 (20.4%) missing values Missing
coveredAreaSqft has 723 (2.5%) missing values Missing
uuid has unique values Unique
floorNumber has 401 (1.4%) zeros Zeros

Reproduction

Analysis started2025-01-21 08:49:06.728839
Analysis finished2025-01-21 08:49:19.447799
Duration12.72 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct897
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-01-21T14:19:19.662790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length49
Median length38
Mean length12.376117
Min length3

Characters and Unicode

Total characters353202
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique300 ?
Unique (%)1.1%

Sample

1st rowNIBM Road
2nd rowHinjewadi
3rd rowTalegaon Dabhade
4th rowPimple Saudagar, Pimpri Chinchwad
5th rowHinjewadi
ValueCountFrequency (%)
chinchwad 2729
 
5.3%
pimpri 2695
 
5.2%
nagar 2571
 
5.0%
road 2096
 
4.1%
kharadi 1972
 
3.8%
hinjewadi 1532
 
3.0%
baner 1266
 
2.5%
wakad 1170
 
2.3%
wagholi 1169
 
2.3%
hadapsar 1152
 
2.2%
Other values (810) 33224
64.4%
2025-01-21T14:19:19.965406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 58995
16.7%
i 27363
 
7.7%
23037
 
6.5%
h 22052
 
6.2%
n 21349
 
6.0%
d 21033
 
6.0%
r 20649
 
5.8%
e 15873
 
4.5%
o 12642
 
3.6%
w 10212
 
2.9%
Other values (51) 119997
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 353202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 58995
16.7%
i 27363
 
7.7%
23037
 
6.5%
h 22052
 
6.2%
n 21349
 
6.0%
d 21033
 
6.0%
r 20649
 
5.8%
e 15873
 
4.5%
o 12642
 
3.6%
w 10212
 
2.9%
Other values (51) 119997
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 353202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 58995
16.7%
i 27363
 
7.7%
23037
 
6.5%
h 22052
 
6.2%
n 21349
 
6.0%
d 21033
 
6.0%
r 20649
 
5.8%
e 15873
 
4.5%
o 12642
 
3.6%
w 10212
 
2.9%
Other values (51) 119997
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 353202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 58995
16.7%
i 27363
 
7.7%
23037
 
6.5%
h 22052
 
6.2%
n 21349
 
6.0%
d 21033
 
6.0%
r 20649
 
5.8%
e 15873
 
4.5%
o 12642
 
3.6%
w 10212
 
2.9%
Other values (51) 119997
34.0%

price
Real number (ℝ)

High correlation 

Distinct3446
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11307983
Minimum600000
Maximum3.5058155 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-01-21T14:19:20.083048image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum600000
5-th percentile2500000
Q14600000
median7500000
Q312000000
95-th percentile32500000
Maximum3.5058155 × 108
Range3.4998155 × 108
Interquartile range (IQR)7400000

Descriptive statistics

Standard deviation14402953
Coefficient of variation (CV)1.2736977
Kurtosis50.257113
Mean11307983
Median Absolute Deviation (MAD)3300000
Skewness5.5795586
Sum3.2271854 × 1011
Variance2.0744506 × 1014
MonotonicityNot monotonic
2025-01-21T14:19:20.207644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6500000 631
 
2.2%
4500000 622
 
2.2%
7500000 589
 
2.1%
3500000 552
 
1.9%
4000000 524
 
1.8%
6000000 484
 
1.7%
5500000 484
 
1.7%
8500000 467
 
1.6%
7000000 459
 
1.6%
8000000 458
 
1.6%
Other values (3436) 23269
81.5%
ValueCountFrequency (%)
600000 1
 
< 0.1%
800000 2
 
< 0.1%
850000 3
 
< 0.1%
900000 2
 
< 0.1%
950000 2
 
< 0.1%
1000000 13
< 0.1%
1100000 9
< 0.1%
1150000 4
 
< 0.1%
1180000 1
 
< 0.1%
1198000 1
 
< 0.1%
ValueCountFrequency (%)
350581554 1
< 0.1%
278382015 1
< 0.1%
230000000 1
< 0.1%
210000000 1
< 0.1%
208821228 1
< 0.1%
202335000 1
< 0.1%
170100000 1
< 0.1%
170000000 2
< 0.1%
165000000 1
< 0.1%
164000000 1
< 0.1%

carpetArea
Real number (ℝ)

High correlation  Missing 

Distinct1804
Distinct (%)7.9%
Missing5695
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean955.30113
Minimum135
Maximum9528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-01-21T14:19:20.332548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile405.15
Q1624
median800
Q31100
95-th percentile2020
Maximum9528
Range9393
Interquartile range (IQR)476

Descriptive statistics

Standard deviation630.9417
Coefficient of variation (CV)0.66046369
Kurtosis21.499816
Mean955.30113
Median Absolute Deviation (MAD)230
Skewness3.6729675
Sum21822899
Variance398087.43
MonotonicityNot monotonic
2025-01-21T14:19:20.446515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850 412
 
1.4%
800 356
 
1.2%
750 342
 
1.2%
900 287
 
1.0%
720 263
 
0.9%
1200 238
 
0.8%
1250 220
 
0.8%
950 217
 
0.8%
700 214
 
0.7%
1100 201
 
0.7%
Other values (1794) 20094
70.4%
(Missing) 5695
 
20.0%
ValueCountFrequency (%)
135 1
< 0.1%
144 1
< 0.1%
147 1
< 0.1%
162 1
< 0.1%
165 1
< 0.1%
166 1
< 0.1%
170 1
< 0.1%
180 1
< 0.1%
181 1
< 0.1%
186 1
< 0.1%
ValueCountFrequency (%)
9528 1
 
< 0.1%
8000 1
 
< 0.1%
7680 1
 
< 0.1%
7650 3
< 0.1%
7640 1
 
< 0.1%
7461 2
< 0.1%
7255 1
 
< 0.1%
7200 1
 
< 0.1%
7000 2
< 0.1%
6900 1
 
< 0.1%

coveredArea
Real number (ℝ)

High correlation  Missing 

Distinct2324
Distinct (%)8.4%
Missing723
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean1234.9181
Minimum200
Maximum13254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-01-21T14:19:20.568024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile530
Q1755
median1050
Q31400
95-th percentile2700
Maximum13254
Range13054
Interquartile range (IQR)645

Descriptive statistics

Standard deviation842.45448
Coefficient of variation (CV)0.68219459
Kurtosis21.088353
Mean1234.9181
Median Absolute Deviation (MAD)320
Skewness3.668837
Sum34350483
Variance709729.54
MonotonicityNot monotonic
2025-01-21T14:19:20.881597image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100 679
 
2.4%
1000 576
 
2.0%
1050 455
 
1.6%
1200 452
 
1.6%
600 430
 
1.5%
650 406
 
1.4%
1150 364
 
1.3%
900 362
 
1.3%
550 338
 
1.2%
850 335
 
1.2%
Other values (2314) 23419
82.1%
(Missing) 723
 
2.5%
ValueCountFrequency (%)
200 4
< 0.1%
204 1
 
< 0.1%
220 1
 
< 0.1%
222 1
 
< 0.1%
224 1
 
< 0.1%
227 1
 
< 0.1%
228 1
 
< 0.1%
230 1
 
< 0.1%
231 1
 
< 0.1%
250 4
< 0.1%
ValueCountFrequency (%)
13254 1
 
< 0.1%
12000 1
 
< 0.1%
11000 1
 
< 0.1%
10380 1
 
< 0.1%
10072 1
 
< 0.1%
9794 1
 
< 0.1%
9734 3
< 0.1%
9600 2
< 0.1%
9345 1
 
< 0.1%
9200 1
 
< 0.1%

carpetAreaSqft
Real number (ℝ)

High correlation  Missing 

Distinct8723
Distinct (%)38.2%
Missing5697
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean10722.456
Minimum2545
Maximum52426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-01-21T14:19:20.996241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2545
5-th percentile5636.05
Q18148
median10031
Q312514.5
95-th percentile17895
Maximum52426
Range49881
Interquartile range (IQR)4366.5

Descriptive statistics

Standard deviation3971.4351
Coefficient of variation (CV)0.37038485
Kurtosis5.8465369
Mean10722.456
Median Absolute Deviation (MAD)2141
Skewness1.5973312
Sum2.4492233 × 108
Variance15772297
MonotonicityNot monotonic
2025-01-21T14:19:21.105801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 253
 
0.9%
8333 96
 
0.3%
11111 94
 
0.3%
12500 82
 
0.3%
12000 64
 
0.2%
6944 63
 
0.2%
9259 60
 
0.2%
10417 59
 
0.2%
13889 59
 
0.2%
6667 52
 
0.2%
Other values (8713) 21960
76.9%
(Missing) 5697
 
20.0%
ValueCountFrequency (%)
2545 1
 
< 0.1%
2663 1
 
< 0.1%
2667 1
 
< 0.1%
2785 1
 
< 0.1%
2903 1
 
< 0.1%
2909 1
 
< 0.1%
2917 1
 
< 0.1%
3000 4
< 0.1%
3107 1
 
< 0.1%
3125 1
 
< 0.1%
ValueCountFrequency (%)
52426 1
< 0.1%
48792 1
< 0.1%
48771 1
< 0.1%
48732 1
< 0.1%
47835 1
< 0.1%
47825 1
< 0.1%
43396 1
< 0.1%
37471 1
< 0.1%
37078 1
< 0.1%
36795 1
< 0.1%

possessionStatus
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing5711
Missing (%)20.0%
Memory size1.4 MiB
Ready To Move
22811 
Under Construction
 
17

Length

Max length18
Median length13
Mean length13.003723
Min length13

Characters and Unicode

Total characters296849
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReady To Move
2nd rowReady To Move
3rd rowReady To Move
4th rowReady To Move
5th rowReady To Move

Common Values

ValueCountFrequency (%)
Ready To Move 22811
79.9%
Under Construction 17
 
0.1%
(Missing) 5711
 
20.0%

Length

2025-01-21T14:19:21.233300image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T14:19:21.335330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ready 22811
33.3%
to 22811
33.3%
move 22811
33.3%
under 17
 
< 0.1%
construction 17
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 45656
15.4%
45639
15.4%
e 45639
15.4%
d 22828
7.7%
R 22811
7.7%
a 22811
7.7%
y 22811
7.7%
T 22811
7.7%
M 22811
7.7%
v 22811
7.7%
Other values (9) 221
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 296849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 45656
15.4%
45639
15.4%
e 45639
15.4%
d 22828
7.7%
R 22811
7.7%
a 22811
7.7%
y 22811
7.7%
T 22811
7.7%
M 22811
7.7%
v 22811
7.7%
Other values (9) 221
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 296849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 45656
15.4%
45639
15.4%
e 45639
15.4%
d 22828
7.7%
R 22811
7.7%
a 22811
7.7%
y 22811
7.7%
T 22811
7.7%
M 22811
7.7%
v 22811
7.7%
Other values (9) 221
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 296849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 45656
15.4%
45639
15.4%
e 45639
15.4%
d 22828
7.7%
R 22811
7.7%
a 22811
7.7%
y 22811
7.7%
T 22811
7.7%
M 22811
7.7%
v 22811
7.7%
Other values (9) 221
 
0.1%

floorNumber
Real number (ℝ)

High correlation  Zeros 

Distinct39
Distinct (%)0.1%
Missing207
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean6.4809403
Minimum-2
Maximum39
Zeros401
Zeros (%)1.4%
Negative253
Negative (%)0.9%
Memory size1.5 MiB
2025-01-21T14:19:21.434021image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile1
Q13
median5
Q39
95-th percentile16
Maximum39
Range41
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.9025472
Coefficient of variation (CV)0.75645616
Kurtosis2.377546
Mean6.4809403
Median Absolute Deviation (MAD)3
Skewness1.3191658
Sum183618
Variance24.034969
MonotonicityNot monotonic
2025-01-21T14:19:21.554459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
5 3210
11.2%
3 2948
10.3%
2 2870
10.1%
4 2682
9.4%
1 2526
8.9%
7 1868
 
6.5%
6 1822
 
6.4%
10 1713
 
6.0%
9 1624
 
5.7%
8 1587
 
5.6%
Other values (29) 5482
19.2%
ValueCountFrequency (%)
-2 88
 
0.3%
-1 165
 
0.6%
0 401
 
1.4%
1 2526
8.9%
2 2870
10.1%
3 2948
10.3%
4 2682
9.4%
5 3210
11.2%
6 1822
6.4%
7 1868
6.5%
ValueCountFrequency (%)
39 1
 
< 0.1%
38 3
 
< 0.1%
34 1
 
< 0.1%
33 2
 
< 0.1%
32 6
 
< 0.1%
31 6
 
< 0.1%
30 17
0.1%
29 7
 
< 0.1%
28 19
0.1%
27 24
0.1%

totalFloorNumber
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)0.2%
Missing64
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean12.806673
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-01-21T14:19:21.926090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median12
Q316
95-th percentile28
Maximum46
Range45
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.369112
Coefficient of variation (CV)0.57541192
Kurtosis0.61602279
Mean12.806673
Median Absolute Deviation (MAD)5
Skewness1.0026023
Sum364670
Variance54.303812
MonotonicityNot monotonic
2025-01-21T14:19:22.042787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
12 3702
 
13.0%
11 2817
 
9.9%
7 2120
 
7.4%
4 1823
 
6.4%
5 1763
 
6.2%
6 1514
 
5.3%
22 1460
 
5.1%
10 1284
 
4.5%
14 1234
 
4.3%
13 1087
 
3.8%
Other values (36) 9671
33.9%
ValueCountFrequency (%)
1 54
 
0.2%
2 108
 
0.4%
3 661
 
2.3%
4 1823
6.4%
5 1763
6.2%
6 1514
5.3%
7 2120
7.4%
8 1066
3.7%
9 976
3.4%
10 1284
4.5%
ValueCountFrequency (%)
46 2
 
< 0.1%
45 9
 
< 0.1%
44 1
 
< 0.1%
43 8
 
< 0.1%
42 2
 
< 0.1%
41 7
 
< 0.1%
40 7
 
< 0.1%
39 8
 
< 0.1%
38 6
 
< 0.1%
37 27
0.1%

transactionType
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size1.4 MiB
Resale
21725 
New Property
6810 

Length

Max length12
Median length6
Mean length7.4319257
Min length6

Characters and Unicode

Total characters212070
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResale
2nd rowResale
3rd rowResale
4th rowNew Property
5th rowNew Property

Common Values

ValueCountFrequency (%)
Resale 21725
76.1%
New Property 6810
 
23.9%
(Missing) 4
 
< 0.1%

Length

2025-01-21T14:19:22.149088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T14:19:22.234748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
resale 21725
61.5%
new 6810
 
19.3%
property 6810
 
19.3%

Most occurring characters

ValueCountFrequency (%)
e 57070
26.9%
R 21725
 
10.2%
s 21725
 
10.2%
a 21725
 
10.2%
l 21725
 
10.2%
r 13620
 
6.4%
N 6810
 
3.2%
w 6810
 
3.2%
6810
 
3.2%
P 6810
 
3.2%
Other values (4) 27240
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 212070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 57070
26.9%
R 21725
 
10.2%
s 21725
 
10.2%
a 21725
 
10.2%
l 21725
 
10.2%
r 13620
 
6.4%
N 6810
 
3.2%
w 6810
 
3.2%
6810
 
3.2%
P 6810
 
3.2%
Other values (4) 27240
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 212070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 57070
26.9%
R 21725
 
10.2%
s 21725
 
10.2%
a 21725
 
10.2%
l 21725
 
10.2%
r 13620
 
6.4%
N 6810
 
3.2%
w 6810
 
3.2%
6810
 
3.2%
P 6810
 
3.2%
Other values (4) 27240
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 212070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 57070
26.9%
R 21725
 
10.2%
s 21725
 
10.2%
a 21725
 
10.2%
l 21725
 
10.2%
r 13620
 
6.4%
N 6810
 
3.2%
w 6810
 
3.2%
6810
 
3.2%
P 6810
 
3.2%
Other values (4) 27240
12.8%

ownershipType
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing12342
Missing (%)43.2%
Memory size1.4 MiB
Freehold
11961 
Co-operative Society
3904 
Leasehold
 
270
Power Of Attorney
 
62

Length

Max length20
Median length8
Mean length10.943508
Min length8

Characters and Unicode

Total characters177252
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFreehold
2nd rowFreehold
3rd rowFreehold
4th rowLeasehold
5th rowFreehold

Common Values

ValueCountFrequency (%)
Freehold 11961
41.9%
Co-operative Society 3904
 
13.7%
Leasehold 270
 
0.9%
Power Of Attorney 62
 
0.2%
(Missing) 12342
43.2%

Length

2025-01-21T14:19:22.331261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T14:19:22.435851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
freehold 11961
59.1%
co-operative 3904
 
19.3%
society 3904
 
19.3%
leasehold 270
 
1.3%
power 62
 
0.3%
of 62
 
0.3%
attorney 62
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 36298
20.5%
o 24067
13.6%
r 15989
9.0%
d 12231
 
6.9%
h 12231
 
6.9%
l 12231
 
6.9%
F 11961
 
6.7%
t 7932
 
4.5%
i 7808
 
4.4%
a 4174
 
2.4%
Other values (16) 32330
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 36298
20.5%
o 24067
13.6%
r 15989
9.0%
d 12231
 
6.9%
h 12231
 
6.9%
l 12231
 
6.9%
F 11961
 
6.7%
t 7932
 
4.5%
i 7808
 
4.4%
a 4174
 
2.4%
Other values (16) 32330
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 36298
20.5%
o 24067
13.6%
r 15989
9.0%
d 12231
 
6.9%
h 12231
 
6.9%
l 12231
 
6.9%
F 11961
 
6.7%
t 7932
 
4.5%
i 7808
 
4.4%
a 4174
 
2.4%
Other values (16) 32330
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 36298
20.5%
o 24067
13.6%
r 15989
9.0%
d 12231
 
6.9%
h 12231
 
6.9%
l 12231
 
6.9%
F 11961
 
6.7%
t 7932
 
4.5%
i 7808
 
4.4%
a 4174
 
2.4%
Other values (16) 32330
18.2%

furnished
Categorical

Distinct3
Distinct (%)< 0.1%
Missing57
Missing (%)0.2%
Memory size1.3 MiB
Unfurnished
17134 
Semi-Furnished
8560 
Furnished
2788 

Length

Max length14
Median length11
Mean length11.705849
Min length9

Characters and Unicode

Total characters333406
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnfurnished
2nd rowUnfurnished
3rd rowUnfurnished
4th rowUnfurnished
5th rowUnfurnished

Common Values

ValueCountFrequency (%)
Unfurnished 17134
60.0%
Semi-Furnished 8560
30.0%
Furnished 2788
 
9.8%
(Missing) 57
 
0.2%

Length

2025-01-21T14:19:22.552543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T14:19:22.653085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
unfurnished 17134
60.2%
semi-furnished 8560
30.1%
furnished 2788
 
9.8%

Most occurring characters

ValueCountFrequency (%)
n 45616
13.7%
i 37042
11.1%
e 37042
11.1%
r 28482
8.5%
s 28482
8.5%
u 28482
8.5%
h 28482
8.5%
d 28482
8.5%
f 17134
 
5.1%
U 17134
 
5.1%
Other values (4) 37028
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 333406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 45616
13.7%
i 37042
11.1%
e 37042
11.1%
r 28482
8.5%
s 28482
8.5%
u 28482
8.5%
h 28482
8.5%
d 28482
8.5%
f 17134
 
5.1%
U 17134
 
5.1%
Other values (4) 37028
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 333406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 45616
13.7%
i 37042
11.1%
e 37042
11.1%
r 28482
8.5%
s 28482
8.5%
u 28482
8.5%
h 28482
8.5%
d 28482
8.5%
f 17134
 
5.1%
U 17134
 
5.1%
Other values (4) 37028
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 333406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 45616
13.7%
i 37042
11.1%
e 37042
11.1%
r 28482
8.5%
s 28482
8.5%
u 28482
8.5%
h 28482
8.5%
d 28482
8.5%
f 17134
 
5.1%
U 17134
 
5.1%
Other values (4) 37028
11.1%

bedrooms
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1881636
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-01-21T14:19:22.736215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88249978
Coefficient of variation (CV)0.40330613
Kurtosis0.92833921
Mean2.1881636
Median Absolute Deviation (MAD)1
Skewness0.6553796
Sum62448
Variance0.77880586
MonotonicityNot monotonic
2025-01-21T14:19:22.816660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 13339
46.7%
3 7011
24.6%
1 6116
21.4%
4 1801
 
6.3%
5 231
 
0.8%
6 32
 
0.1%
7 5
 
< 0.1%
8 3
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
1 6116
21.4%
2 13339
46.7%
3 7011
24.6%
4 1801
 
6.3%
5 231
 
0.8%
6 32
 
0.1%
7 5
 
< 0.1%
8 3
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
8 3
 
< 0.1%
7 5
 
< 0.1%
6 32
 
0.1%
5 231
 
0.8%
4 1801
 
6.3%
3 7011
24.6%
2 13339
46.7%
1 6116
21.4%

bathrooms
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1990609
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-01-21T14:19:22.898193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93757304
Coefficient of variation (CV)0.42635155
Kurtosis2.7801044
Mean2.1990609
Median Absolute Deviation (MAD)0
Skewness1.1616793
Sum62759
Variance0.8790432
MonotonicityNot monotonic
2025-01-21T14:19:22.983869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 14470
50.7%
3 6016
21.1%
1 5801
20.3%
4 1503
 
5.3%
5 582
 
2.0%
6 139
 
0.5%
7 19
 
0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
1 5801
20.3%
2 14470
50.7%
3 6016
21.1%
4 1503
 
5.3%
5 582
 
2.0%
6 139
 
0.5%
7 19
 
0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 2
 
< 0.1%
9 2
 
< 0.1%
8 4
 
< 0.1%
7 19
 
0.1%
6 139
 
0.5%
5 582
 
2.0%
4 1503
 
5.3%
3 6016
21.1%
2 14470
50.7%

propertyType
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Multistorey Apartment
27623 
Builder Floor Apartment
 
769
Penthouse
 
147

Length

Max length23
Median length21
Mean length20.992081
Min length9

Characters and Unicode

Total characters599093
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMultistorey Apartment
2nd rowMultistorey Apartment
3rd rowBuilder Floor Apartment
4th rowMultistorey Apartment
5th rowMultistorey Apartment

Common Values

ValueCountFrequency (%)
Multistorey Apartment 27623
96.8%
Builder Floor Apartment 769
 
2.7%
Penthouse 147
 
0.5%

Length

2025-01-21T14:19:23.083681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T14:19:23.169464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
apartment 28392
49.2%
multistorey 27623
47.9%
builder 769
 
1.3%
floor 769
 
1.3%
penthouse 147
 
0.3%

Most occurring characters

ValueCountFrequency (%)
t 112177
18.7%
r 57553
 
9.6%
e 57078
 
9.5%
o 29308
 
4.9%
l 29161
 
4.9%
29161
 
4.9%
n 28539
 
4.8%
u 28539
 
4.8%
i 28392
 
4.7%
m 28392
 
4.7%
Other values (11) 170793
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 599093
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 112177
18.7%
r 57553
 
9.6%
e 57078
 
9.5%
o 29308
 
4.9%
l 29161
 
4.9%
29161
 
4.9%
n 28539
 
4.8%
u 28539
 
4.8%
i 28392
 
4.7%
m 28392
 
4.7%
Other values (11) 170793
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 599093
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 112177
18.7%
r 57553
 
9.6%
e 57078
 
9.5%
o 29308
 
4.9%
l 29161
 
4.9%
29161
 
4.9%
n 28539
 
4.8%
u 28539
 
4.8%
i 28392
 
4.7%
m 28392
 
4.7%
Other values (11) 170793
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 599093
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 112177
18.7%
r 57553
 
9.6%
e 57078
 
9.5%
o 29308
 
4.9%
l 29161
 
4.9%
29161
 
4.9%
n 28539
 
4.8%
u 28539
 
4.8%
i 28392
 
4.7%
m 28392
 
4.7%
Other values (11) 170793
28.5%

ageofcons
Categorical

High correlation  Missing 

Distinct7
Distinct (%)< 0.1%
Missing3999
Missing (%)14.0%
Memory size1.3 MiB
Less than 5 years
7623 
Under Construction
5644 
5 to 10 years
5208 
New Construction
3776 
10 to 15 years
1621 
Other values (2)
 
668

Length

Max length18
Median length17
Mean length15.947392
Min length13

Characters and Unicode

Total characters391349
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15 to 20 years
2nd rowLess than 5 years
3rd rowNew Construction
4th rowNew Construction
5th rowNew Construction

Common Values

ValueCountFrequency (%)
Less than 5 years 7623
26.7%
Under Construction 5644
19.8%
5 to 10 years 5208
18.2%
New Construction 3776
13.2%
10 to 15 years 1621
 
5.7%
15 to 20 years 404
 
1.4%
Above 20 years 264
 
0.9%
(Missing) 3999
14.0%

Length

2025-01-21T14:19:23.270340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T14:19:23.364134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
years 15120
19.1%
5 12831
16.2%
construction 9420
11.9%
than 7623
9.6%
less 7623
9.6%
to 7233
9.1%
10 6829
8.6%
under 5644
 
7.1%
new 3776
 
4.8%
15 2025
 
2.6%
Other values (2) 932
 
1.2%

Most occurring characters

ValueCountFrequency (%)
54516
13.9%
s 39786
10.2%
t 33696
 
8.6%
e 32427
 
8.3%
n 32107
 
8.2%
r 30184
 
7.7%
o 26337
 
6.7%
a 22743
 
5.8%
y 15120
 
3.9%
5 14856
 
3.8%
Other values (16) 89577
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 391349
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
54516
13.9%
s 39786
10.2%
t 33696
 
8.6%
e 32427
 
8.3%
n 32107
 
8.2%
r 30184
 
7.7%
o 26337
 
6.7%
a 22743
 
5.8%
y 15120
 
3.9%
5 14856
 
3.8%
Other values (16) 89577
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 391349
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
54516
13.9%
s 39786
10.2%
t 33696
 
8.6%
e 32427
 
8.3%
n 32107
 
8.2%
r 30184
 
7.7%
o 26337
 
6.7%
a 22743
 
5.8%
y 15120
 
3.9%
5 14856
 
3.8%
Other values (16) 89577
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 391349
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
54516
13.9%
s 39786
10.2%
t 33696
 
8.6%
e 32427
 
8.3%
n 32107
 
8.2%
r 30184
 
7.7%
o 26337
 
6.7%
a 22743
 
5.8%
y 15120
 
3.9%
5 14856
 
3.8%
Other values (16) 89577
22.9%

noOfLifts
Real number (ℝ)

High correlation  Missing 

Distinct10
Distinct (%)0.1%
Missing15210
Missing (%)53.3%
Infinite0
Infinite (%)0.0%
Mean2.5314727
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-01-21T14:19:23.477771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.96366488
Coefficient of variation (CV)0.38067362
Kurtosis4.0688405
Mean2.5314727
Median Absolute Deviation (MAD)0
Skewness1.2312102
Sum33742
Variance0.92865
MonotonicityNot monotonic
2025-01-21T14:19:23.565660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 6959
24.4%
3 3031
 
10.6%
4 2051
 
7.2%
1 1019
 
3.6%
5 185
 
0.6%
6 52
 
0.2%
7 11
 
< 0.1%
10 11
 
< 0.1%
8 6
 
< 0.1%
9 4
 
< 0.1%
(Missing) 15210
53.3%
ValueCountFrequency (%)
1 1019
 
3.6%
2 6959
24.4%
3 3031
10.6%
4 2051
 
7.2%
5 185
 
0.6%
6 52
 
0.2%
7 11
 
< 0.1%
8 6
 
< 0.1%
9 4
 
< 0.1%
10 11
 
< 0.1%
ValueCountFrequency (%)
10 11
 
< 0.1%
9 4
 
< 0.1%
8 6
 
< 0.1%
7 11
 
< 0.1%
6 52
 
0.2%
5 185
 
0.6%
4 2051
 
7.2%
3 3031
10.6%
2 6959
24.4%
1 1019
 
3.6%

url
Text

Distinct28536
Distinct (%)100.0%
Missing3
Missing (%)< 0.1%
Memory size1.4 MiB
2025-01-21T14:19:23.781621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length176
Median length162
Mean length133.10657
Min length116

Characters and Unicode

Total characters3798329
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28536 ?
Unique (%)100.0%

Sample

1st rowhttps://www.magicbricks.com/propertyDetails/4-BHK-1750-Sq-ft-Multistorey-Apartment-FOR-Sale-NIBM-Road-in-Pune&id=4d423131393133303234
2nd rowhttps://www.magicbricks.com/propertyDetails/3-BHK-1720-Sq-ft-Multistorey-Apartment-FOR-Sale-Hinjewadi-in-Pune-r14&id=4d423132393539303832
3rd rowhttps://www.magicbricks.com/propertyDetails/2-BHK-1100-Sq-ft-Builder-Floor-Apartment-FOR-Sale-Talegaon-Dabhade-in-Pune-r1&id=4d423132393633353032
4th rowhttps://www.magicbricks.com/propertyDetails/2-BHK-1255-Sq-ft-Multistorey-Apartment-FOR-Sale-Pimple-Saudagar-in-Pune&id=4d423134363034333939
5th rowhttps://www.magicbricks.com/propertyDetails/5-BHK-3245-Sq-ft-Multistorey-Apartment-FOR-Sale-Hinjewadi-in-Pune-r15&id=4d423134373133373731
ValueCountFrequency (%)
https://www.magicbricks.com/propertydetails/2-bhk-1100-sq-ft-builder-floor-apartment-for-sale-talegaon-dabhade-in-pune-r1&id=4d423132393633353032 1
 
< 0.1%
https://www.magicbricks.com/propertydetails/5-bhk-3245-sq-ft-multistorey-apartment-for-sale-hinjewadi-in-pune-r15&id=4d423134373133373731 1
 
< 0.1%
https://www.magicbricks.com/propertydetails/3-bhk-1740-sq-ft-multistorey-apartment-for-sale-hinjewadi-in-pune-r15&id=4d423134373736303639 1
 
< 0.1%
https://www.magicbricks.com/propertydetails/2-bhk-855-sq-ft-multistorey-apartment-for-sale-nibm-road-in-pune&id=4d423134373839353133 1
 
< 0.1%
https://www.magicbricks.com/propertydetails/4-bhk-2501-sq-ft-multistorey-apartment-for-sale-pimple-nilakh-in-pune-r15&id=4d423134383533333836 1
 
< 0.1%
https://www.magicbricks.com/propertydetails/2-bhk-1015-sq-ft-multistorey-apartment-for-sale-hadapsar-in-pune&id=4d423135343835363730 1
 
< 0.1%
https://www.magicbricks.com/propertydetails/1-bhk-500-sq-ft-builder-floor-apartment-for-sale-chakan-in-pune-r1&id=4d423136313033353838 1
 
< 0.1%
https://www.magicbricks.com/propertydetails/3-bhk-2401-sq-ft-multistorey-apartment-for-sale-baner-in-pune-r12&id=4d423136313636383736 1
 
< 0.1%
https://www.magicbricks.com/propertydetails/1-bhk-650-sq-ft-multistorey-apartment-for-sale-loni-kalbhor-in-pune&id=4d423136373039363032 1
 
< 0.1%
https://www.magicbricks.com/propertydetails/3-bhk-2400-sq-ft-multistorey-apartment-for-sale-baner-in-pune-r14&id=4d423136373239303132 1
 
< 0.1%
Other values (28526) 28526
> 99.9%
2025-01-21T14:19:24.120450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 328874
 
8.7%
3 261443
 
6.9%
t 259580
 
6.8%
i 188556
 
5.0%
e 185576
 
4.9%
a 164117
 
4.3%
r 160869
 
4.2%
s 117689
 
3.1%
p 116678
 
3.1%
/ 114144
 
3.0%
Other values (56) 1900803
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3798329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 328874
 
8.7%
3 261443
 
6.9%
t 259580
 
6.8%
i 188556
 
5.0%
e 185576
 
4.9%
a 164117
 
4.3%
r 160869
 
4.2%
s 117689
 
3.1%
p 116678
 
3.1%
/ 114144
 
3.0%
Other values (56) 1900803
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3798329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 328874
 
8.7%
3 261443
 
6.9%
t 259580
 
6.8%
i 188556
 
5.0%
e 185576
 
4.9%
a 164117
 
4.3%
r 160869
 
4.2%
s 117689
 
3.1%
p 116678
 
3.1%
/ 114144
 
3.0%
Other values (56) 1900803
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3798329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 328874
 
8.7%
3 261443
 
6.9%
t 259580
 
6.8%
i 188556
 
5.0%
e 185576
 
4.9%
a 164117
 
4.3%
r 160869
 
4.2%
s 117689
 
3.1%
p 116678
 
3.1%
/ 114144
 
3.0%
Other values (56) 1900803
50.0%

uuid
Text

Unique 

Distinct28539
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-01-21T14:19:24.279672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters627858
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28539 ?
Unique (%)100.0%

Sample

1st rowfS2hgazbhZzDRBakmQmqLC
2nd rowhzjrvDEnQoEfcFGNZEhwJf
3rd rowPY4ZQLpgcGPYZySMhfWjG7
4th row8dSUEk42iJzhF49WWRSos2
5th rowFwMkdCde8WiDXXqt8wVugQ
ValueCountFrequency (%)
uldxxvwnjw9tzawntvdu4u 1
 
< 0.1%
crffuzke5durnqlxcbvjf2 1
 
< 0.1%
upjdcuvblqrwwiddkzykhy 1
 
< 0.1%
fs2hgazbhzzdrbakmqmqlc 1
 
< 0.1%
hzjrvdenqoefcfgnzehwjf 1
 
< 0.1%
py4zqlpgcgpyzysmhfwjg7 1
 
< 0.1%
8dsuek42ijzhf49wwrsos2 1
 
< 0.1%
fwmkdcde8widxxqt8wvugq 1
 
< 0.1%
ctfg7g2h9hpjyycledcwdo 1
 
< 0.1%
nskigxewvu2hfgwc5rsqkq 1
 
< 0.1%
Other values (28529) 28529
> 99.9%
2025-01-21T14:19:24.528738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 11339
 
1.8%
j 11316
 
1.8%
5 11310
 
1.8%
b 11292
 
1.8%
G 11262
 
1.8%
f 11230
 
1.8%
d 11228
 
1.8%
S 11218
 
1.8%
A 11217
 
1.8%
L 11211
 
1.8%
Other values (47) 515235
82.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 627858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 11339
 
1.8%
j 11316
 
1.8%
5 11310
 
1.8%
b 11292
 
1.8%
G 11262
 
1.8%
f 11230
 
1.8%
d 11228
 
1.8%
S 11218
 
1.8%
A 11217
 
1.8%
L 11211
 
1.8%
Other values (47) 515235
82.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 627858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 11339
 
1.8%
j 11316
 
1.8%
5 11310
 
1.8%
b 11292
 
1.8%
G 11262
 
1.8%
f 11230
 
1.8%
d 11228
 
1.8%
S 11218
 
1.8%
A 11217
 
1.8%
L 11211
 
1.8%
Other values (47) 515235
82.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 627858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 11339
 
1.8%
j 11316
 
1.8%
5 11310
 
1.8%
b 11292
 
1.8%
G 11262
 
1.8%
f 11230
 
1.8%
d 11228
 
1.8%
S 11218
 
1.8%
A 11217
 
1.8%
L 11211
 
1.8%
Other values (47) 515235
82.1%

ReservedParking
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing5824
Missing (%)20.4%
Memory size1.4 MiB
True
20163 
False
2552 
(Missing)
5824 
ValueCountFrequency (%)
True 20163
70.7%
False 2552
 
8.9%
(Missing) 5824
 
20.4%
2025-01-21T14:19:24.616427image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

coveredAreaSqft
Real number (ℝ)

High correlation  Missing 

Distinct12984
Distinct (%)46.7%
Missing723
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean7878.9808
Minimum2003.643
Maximum37665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-01-21T14:19:24.701047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2003.643
5-th percentile4000
Q15818.1818
median7304.2323
Q39285.7143
95-th percentile13700.656
Maximum37665
Range35661.357
Interquartile range (IQR)3467.5325

Descriptive statistics

Standard deviation3124.2265
Coefficient of variation (CV)0.39652673
Kurtosis4.9684427
Mean7878.9808
Median Absolute Deviation (MAD)1695.7677
Skewness1.5998242
Sum2.1916173 × 108
Variance9760791
MonotonicityNot monotonic
2025-01-21T14:19:24.817583image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000.0 291
 
1.0%
5000.0 211
 
0.7%
6000.0 179
 
0.6%
7500.0 169
 
0.6%
8000.0 163
 
0.6%
6666.666666666667 158
 
0.6%
7000.0 127
 
0.4%
8333.333333333334 113
 
0.4%
7142.857142857143 97
 
0.3%
4000.0 96
 
0.3%
Other values (12974) 26212
91.8%
(Missing) 723
 
2.5%
ValueCountFrequency (%)
2003.6429872495446 1
< 0.1%
2158.273381294964 1
< 0.1%
2227.2727272727275 1
< 0.1%
2289.156626506024 1
< 0.1%
2351.0971786833857 1
< 0.1%
2363.6363636363635 1
< 0.1%
2375.296912114014 1
< 0.1%
2380.9523809523807 1
< 0.1%
2413.793103448276 1
< 0.1%
2448.657187993681 1
< 0.1%
ValueCountFrequency (%)
37665.0 1
 
< 0.1%
35000.0 3
< 0.1%
34300.0 2
 
< 0.1%
31172.0 1
 
< 0.1%
29245.283018867925 1
 
< 0.1%
29059.82905982906 1
 
< 0.1%
28947.36842105263 1
 
< 0.1%
28436.018957345972 1
 
< 0.1%
27380.95238095238 1
 
< 0.1%
27350.42735042735 5
< 0.1%

Interactions

2025-01-21T14:19:17.677712image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:09.362168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:10.313080image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:11.184367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:12.070208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:13.202917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:14.138196image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:15.037368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:15.958313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:16.850568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:17.771235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:09.488986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:10.403638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:11.277080image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:12.161980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:13.311409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:14.231714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:15.137986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:16.049786image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:16.940640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:17.857816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:09.586648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:10.484807image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:11.362228image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:12.245511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:13.436210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:14.315330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:15.222513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:16.130655image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:17.020506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:17.950548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:09.690154image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:10.585592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:11.452734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:12.350308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:13.539186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:14.405018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:15.318414image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:16.253206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:17.104038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:18.035554image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:09.782675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:10.677257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:11.542886image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:12.449314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:13.635190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:14.489575image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:15.412108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:16.346754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:17.184492image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:18.121768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:09.869345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:10.759777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:11.634985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:12.538146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:13.717719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:14.583111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:15.503291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:16.428796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:17.261087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:18.214939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:09.955254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:10.840569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:11.724600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:12.630076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:13.802079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:14.666694image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:15.612852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:16.507132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:17.338172image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:18.304142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:10.041041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:10.931681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:11.806277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:12.734509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:13.883204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:14.772228image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:15.692170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:16.603658image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:17.426688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:18.391298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:10.125090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:11.010381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:11.889729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:13.015707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:13.967654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:14.863731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:15.771695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:16.680191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:17.501228image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:18.487163image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:10.214629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:11.091972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:11.973861image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:13.105266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:14.051523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:14.948276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:15.866253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:16.758849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-21T14:19:17.583736image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-21T14:19:24.907268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ReservedParkingageofconsbathroomsbedroomscarpetAreacarpetAreaSqftcoveredAreacoveredAreaSqftfloorNumberfurnishednoOfLiftsownershipTypepossessionStatuspricepropertyTypetotalFloorNumbertransactionType
ReservedParking1.0000.0740.1050.1100.0880.1270.0920.1250.0820.0560.0730.0231.0000.0490.0490.1340.013
ageofcons0.0741.0000.1090.1030.0940.0980.0870.0970.1640.1730.2110.1931.0000.0180.0420.2620.821
bathrooms0.1050.1091.0000.9240.8710.5070.8770.5240.3200.0430.1540.0480.1510.8180.1690.4300.253
bedrooms0.1100.1030.9241.0000.8950.5040.9000.5180.3070.0380.1630.0310.1790.8310.1680.4180.241
carpetArea0.0880.0940.8710.8951.0000.5510.9810.5810.3140.0610.1210.0440.1440.9110.2310.4190.190
carpetAreaSqft0.1270.0980.5070.5040.5511.0000.5580.9700.2620.0820.1510.0610.1250.8290.0630.3860.193
coveredArea0.0920.0870.8770.9000.9810.5581.0000.5470.3410.0560.1330.0320.0000.9060.2040.4590.182
coveredAreaSqft0.1250.0970.5240.5180.5810.9700.5471.0000.2610.0970.1030.0660.0000.8350.1300.3800.181
floorNumber0.0820.1640.3200.3070.3140.2620.3410.2611.0000.0600.3940.0941.0000.3470.1380.6430.326
furnished0.0560.1730.0430.0380.0610.0820.0560.0970.0601.0000.0930.0660.0120.0490.0280.0960.186
noOfLifts0.0730.2110.1540.1630.1210.1510.1330.1030.3940.0931.0000.1620.0000.1370.0610.6150.370
ownershipType0.0230.1930.0480.0310.0440.0610.0320.0660.0940.0660.1621.0001.0000.0300.0330.1440.281
possessionStatus1.0001.0000.1510.1790.1440.1250.0000.0001.0000.0120.0001.0001.0000.1270.0621.0000.091
price0.0490.0180.8180.8310.9110.8290.9060.8350.3470.0490.1370.0300.1271.0000.1350.4830.033
propertyType0.0490.0420.1690.1680.2310.0630.2040.1300.1380.0280.0610.0330.0620.1351.0000.2090.078
totalFloorNumber0.1340.2620.4300.4180.4190.3860.4590.3800.6430.0960.6150.1441.0000.4830.2091.0000.500
transactionType0.0130.8210.2530.2410.1900.1930.1820.1810.3260.1860.3700.2810.0910.0330.0780.5001.000

Missing values

2025-01-21T14:19:18.649535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-21T14:19:18.937032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-21T14:19:19.269830image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

localityNamepricecarpetAreacoveredAreacarpetAreaSqftpossessionStatusfloorNumbertotalFloorNumbertransactionTypeownershipTypefurnishedbedroomsbathroomspropertyTypeageofconsnoOfLiftsurluuidReservedParkingcoveredAreaSqft
propertyId
11913024NIBM Road8500000175017504857Ready To Move14ResaleFreeholdUnfurnished44Multistorey Apartment15 to 20 years<NA>https://www.magicbricks.com/propertyDetails/4-BHK-1750-Sq-ft-Multistorey-Apartment-FOR-Sale-NIBM-Road-in-Pune&id=4d423131393133303234fS2hgazbhZzDRBakmQmqLCTrue4857.142857
12959082Hinjewadi166000001374172012082Ready To Move1330ResaleFreeholdUnfurnished33Multistorey ApartmentLess than 5 years3https://www.magicbricks.com/propertyDetails/3-BHK-1720-Sq-ft-Multistorey-Apartment-FOR-Sale-Hinjewadi-in-Pune-r14&id=4d423132393539303832hzjrvDEnQoEfcFGNZEhwJfTrue9651.162791
12963502Talegaon Dabhade300000083611003589Ready To Move26ResaleFreeholdUnfurnished22Builder Floor ApartmentNaN2https://www.magicbricks.com/propertyDetails/2-BHK-1100-Sq-ft-Builder-Floor-Apartment-FOR-Sale-Talegaon-Dabhade-in-Pune-r1&id=4d423132393633353032PY4ZQLpgcGPYZySMhfWjG7True2727.272727
14604399Pimple Saudagar, Pimpri Chinchwad8500000<NA>1255<NA>Ready To Move<NA>12New PropertyLeaseholdUnfurnished22Multistorey ApartmentNaN<NA>https://www.magicbricks.com/propertyDetails/2-BHK-1255-Sq-ft-Multistorey-Apartment-FOR-Sale-Pimple-Saudagar-in-Pune&id=4d4231343630343339398dSUEk42iJzhF49WWRSos2False6772.908367
14713771Hinjewadi351000002268324515476Ready To Move2125New PropertyFreeholdUnfurnished55Multistorey ApartmentNaN3https://www.magicbricks.com/propertyDetails/5-BHK-3245-Sq-ft-Multistorey-Apartment-FOR-Sale-Hinjewadi-in-Pune-r15&id=4d423134373133373731FwMkdCde8WiDXXqt8wVugQTrue10816.640986
14776069Hinjewadi147000001200174012250Ready To Move2930ResaleFreeholdUnfurnished33Multistorey ApartmentNew Construction3https://www.magicbricks.com/propertyDetails/3-BHK-1740-Sq-ft-Multistorey-Apartment-FOR-Sale-Hinjewadi-in-Pune-r15&id=4d423134373736303639Ctfg7g2h9HpjYYcLEDcWDoTrue8448.275862
14789513NIBM Road51000007368556929Ready To Move412New PropertyCo-operative SocietyUnfurnished22Multistorey ApartmentNew Construction<NA>https://www.magicbricks.com/propertyDetails/2-BHK-855-Sq-ft-Multistorey-Apartment-FOR-Sale-NIBM-Road-in-Pune&id=4d423134373839353133nSkigxEwvU2hfgwc5RSqkQTrue5964.912281
14853386Pimple Nilakh, Pimpri Chinchwad290000001750250116571Ready To Move412New PropertyFreeholdSemi-Furnished44Multistorey ApartmentNew Construction3https://www.magicbricks.com/propertyDetails/4-BHK-2501-Sq-ft-Multistorey-Apartment-FOR-Sale-Pimple-Nilakh-in-Pune-r15&id=4d4231343835333338364RdwhgXJop3DXUudedx9TFTrue11595.361855
15485670Hadapsar5000000<NA>1015<NA>Ready To Move611ResaleFreeholdSemi-Furnished22Multistorey ApartmentLess than 5 years<NA>https://www.magicbricks.com/propertyDetails/2-BHK-1015-Sq-ft-Multistorey-Apartment-FOR-Sale-Hadapsar-in-Pune&id=4d423135343835363730n3iH5XgeAEJNxVqZdxhSjfFalse4926.108374
16103588Chakan2000000<NA>500<NA>Ready To Move24ResaleFreeholdUnfurnished11Builder Floor ApartmentLess than 5 years<NA>https://www.magicbricks.com/propertyDetails/1-BHK-500-Sq-ft-Builder-Floor-Apartment-FOR-Sale-Chakan-in-Pune-r1&id=4d423136313033353838bGfdEqbJq8p8rHqDyfnz5nTrue4000.0
localityNamepricecarpetAreacoveredAreacarpetAreaSqftpossessionStatusfloorNumbertotalFloorNumbertransactionTypeownershipTypefurnishedbedroomsbathroomspropertyTypeageofconsnoOfLiftsurluuidReservedParkingcoveredAreaSqft
propertyId
76178519Undri60000008009527500Ready To Move511ResaleCo-operative SocietyUnfurnished22Multistorey Apartment5 to 10 years1https://www.magicbricks.com/propertyDetails/2-BHK-952-Sq-ft-Multistorey-Apartment-FOR-Sale-Undri-in-Pune&id=4d423736313738353139jKAPudxAWvdzSBALvctQVpTrue6302.521008
76178801Viman Nagar650000045058014444Ready To Move36ResaleFreeholdFurnished11Multistorey Apartment5 to 10 years1https://www.magicbricks.com/propertyDetails/1-BHK-580-Sq-ft-Multistorey-Apartment-FOR-Sale-Viman-Nagar-in-Pune&id=4d423736313738383031iai9XvnaUVYRNNYrL9ggKuTrue11206.896552
76178907Wakad9300000912121210197Ready To Move412ResaleCo-operative SocietyUnfurnished22Multistorey Apartment5 to 10 years2https://www.magicbricks.com/propertyDetails/2-BHK-1212-Sq-ft-Multistorey-Apartment-FOR-Sale-Wakad-in-Pune&id=4d423736313738393037fuGhoE4sbqYdashmf3yiyjTrue7673.267327
76178939Ravet, Pimpri Chinchwad5500000<NA>600<NA>Ready To Move1111ResaleCo-operative SocietySemi-Furnished11Multistorey ApartmentLess than 5 years<NA>https://www.magicbricks.com/propertyDetails/1-BHK-600-Sq-ft-Multistorey-Apartment-FOR-Sale-Ravet-in-Pune&id=4d423736313738393339WwxUiD4YXamuHrsUYPgkCrTrue9166.666667
76179027Hadapsar810000075095010800Ready To Move89ResaleFreeholdUnfurnished22Multistorey Apartment5 to 10 years<NA>https://www.magicbricks.com/propertyDetails/2-BHK-950-Sq-ft-Multistorey-Apartment-FOR-Sale-Hadapsar-in-Pune&id=4d423736313739303237ADwFymt4fAwAkkiabXdtL2True8526.315789
76179339Magarpatta750000075095010000Ready To Move611ResaleFreeholdUnfurnished22Multistorey Apartment5 to 10 years2https://www.magicbricks.com/propertyDetails/2-BHK-950-Sq-ft-Multistorey-Apartment-FOR-Sale-Magarpatta-City-in-Pune&id=4d4237363137393333393hKsEf4kGYmpSGmbyrkzD7True7894.736842
76181599Hinjewadi650000070010009286Ready To Move57ResaleCo-operative SocietyFurnished22Multistorey Apartment5 to 10 years2https://www.magicbricks.com/propertyDetails/2-BHK-1000-Sq-ft-Multistorey-Apartment-FOR-Sale-Hinjewadi-in-Pune&id=4d4237363138313539392epc4pLrkzwu6oczdy3w6jTrue6500.0
76181823Sus570000054070010556Ready To Move412ResaleCo-operative SocietyUnfurnished12Multistorey Apartment5 to 10 years2https://www.magicbricks.com/propertyDetails/1-BHK-700-Sq-ft-Multistorey-Apartment-FOR-Sale-Sus-in-Pune&id=4d423736313831383233G9KHEcaXNGJohvjUmoChdhTrue8142.857143
76183381Katraj Kondhwa Road49000006308187778Ready To Move812ResaleFreeholdSemi-Furnished22Multistorey ApartmentLess than 5 years<NA>https://www.magicbricks.com/propertyDetails/2-BHK-818-Sq-ft-Multistorey-Apartment-FOR-Sale-Katraj-Kondhwa-Road-in-Pune&id=4d423736313833333831FECWHV28nYtBMAfpX25o7ATrue5990.220049
76183601Chikhali Pimpri Chinchwad4800000<NA>645<NA>Ready To Move811ResaleCo-operative SocietyFurnished12Multistorey Apartment5 to 10 years<NA>https://www.magicbricks.com/propertyDetails/1-BHK-645-Sq-ft-Multistorey-Apartment-FOR-Sale-Chikhali-in-Pune&id=4d423736313833363031UPJDCuVBLQRWWiDdKzYkHYTrue7441.860465